



'''simple docstring'''



import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def        UpperCAmelCase (    A	:					Dict					):
							return 1.0 / (1.0 + np.exp(-_outputs					))
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   str         =      np.max(_outputs       ,		axis=-1       ,		keepdims=A					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.exp(_outputs - maxes					)
							return shifted_exp / shifted_exp.sum(axis=-1       ,		keepdims=A					)
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Optional[Any]						  =					'sigmoid'
						_lowerCAmelCase  : int						  =					'softmax'
						_lowerCAmelCase  : Union[str, Any]						  =					'none'


@add_end_docstrings(
    snake_case_     ,					R'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '     ,					)
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Optional[int]						  =					False
						_lowerCAmelCase  : List[Any]						  =					ClassificationFunction.NONE







						def __init__(	self :   int					,     **lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__					)

													self.check_model_type(
													    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
													    if self.framework == '''tf'''
													    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING					)







						def        __lowercase     (	self :   Union[str, Any]					,     lowerCAmelCase__ :   List[Any]=None					,     lowerCAmelCase__ :   Tuple=None					,     lowerCAmelCase__ :   List[Any]=""					,     **lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													# Using "" as default argument because we're going to use `top_k=None` in user code to declare
													# "No top_k"
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      tokenizer_kwargs

													SCREAMING_SNAKE_CASE       :   Dict         =      {}
													if hasattr(self.model.config					,     '''return_all_scores'''					) and return_all_scores is None:
																				SCREAMING_SNAKE_CASE       :   int         =      self.model.config.return_all_scores

													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					) or top_k is None:
																				SCREAMING_SNAKE_CASE       :   Any         =      top_k
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      False
													elif return_all_scores is not None:
																				warnings.warn(
																				    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
																				    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''					,     lowerCAmelCase__					,     )
																				if return_all_scores:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
																				else:
																											SCREAMING_SNAKE_CASE       :   int         =      1

													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																				SCREAMING_SNAKE_CASE       :   int         =      ClassificationFunction[function_to_apply.upper()]

													if function_to_apply is not None:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      function_to_apply
													return preprocess_params, {}, postprocess_params







						def __call__(	self :   Union[str, Any]					,     *lowerCAmelCase__ :   Optional[Any]					,     **lowerCAmelCase__ :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      super().__call__(*lowerCAmelCase__					,     **lowerCAmelCase__					)
													# TODO try and retrieve it in a nicer way from _sanitize_parameters.
													SCREAMING_SNAKE_CASE       :   Tuple         =      '''top_k''' not in kwargs
													if isinstance(args[0]					,     lowerCAmelCase__					) and _legacy:
																				# This pipeline is odd, and return a list when single item is run
																				return [result]
													else:
																				return result







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   Union[str, Any]					,     **lowerCAmelCase__ :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      self.framework
													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																				return self.tokenizer(**lowerCAmelCase__					,     return_tensors=lowerCAmelCase__					,     **lowerCAmelCase__					)
													elif isinstance(lowerCAmelCase__					,     lowerCAmelCase__					) and len(lowerCAmelCase__					) == 1 and isinstance(inputs[0]					,     lowerCAmelCase__					) and len(inputs[0]					) == 2:
																				# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
																				return self.tokenizer(
																				    text=inputs[0][0]					,     text_pair=inputs[0][1]					,     return_tensors=lowerCAmelCase__					,     **lowerCAmelCase__					)
													elif isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																				# This is likely an invalid usage of the pipeline attempting to pass text pairs.
																				raise ValueError(
																				    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
																				    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''					)
													return self.tokenizer(lowerCAmelCase__					,     return_tensors=lowerCAmelCase__					,     **lowerCAmelCase__					)







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													return self.model(**lowerCAmelCase__					)







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   List[Any]=None					,     lowerCAmelCase__ :   List[Any]=1					,     lowerCAmelCase__ :   int=True					):

													"""simple docstring"""
													# `_legacy` is used to determine if we're running the naked pipeline and in backward
													# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
													# the more natural result containing the list.
													# Default value before `set_parameters`
													if function_to_apply is None:
																				if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      ClassificationFunction.SIGMOID
																				elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      ClassificationFunction.SOFTMAX
																				elif hasattr(self.model.config					,     '''function_to_apply'''					) and function_to_apply is None:
																											SCREAMING_SNAKE_CASE       :   int         =      self.model.config.function_to_apply
																				else:
																											SCREAMING_SNAKE_CASE       :   Dict         =      ClassificationFunction.NONE

													SCREAMING_SNAKE_CASE       :   List[str]         =      model_outputs['''logits'''][0]
													SCREAMING_SNAKE_CASE       :   str         =      outputs.numpy()

													if function_to_apply == ClassificationFunction.SIGMOID:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      sigmoid(lowerCAmelCase__					)
													elif function_to_apply == ClassificationFunction.SOFTMAX:
																				SCREAMING_SNAKE_CASE       :   str         =      softmax(lowerCAmelCase__					)
													elif function_to_apply == ClassificationFunction.NONE:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      outputs
													else:
																				raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}"""					)

													if top_k == 1 and _legacy:
																				return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

													SCREAMING_SNAKE_CASE       :   Tuple         =      [
													    {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(lowerCAmelCase__					)
													]
													if not _legacy:
																				dict_scores.sort(key=lambda lowerCAmelCase__					: x["score"]					,     reverse=lowerCAmelCase__					)
																				if top_k is not None:
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      dict_scores[:top_k]
													return dict_scores









'''simple docstring'''



lowerCAmelCase_  :							List[Any]             =    [0, 2, 4, 6, 8]
lowerCAmelCase_  :							str             =    [1, 3, 5, 7, 9]
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					list[int]       ,		A	:					int					):
							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1       ,		-1       ,		-1					):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														SCREAMING_SNAKE_CASE       :   List[Any]         =      0
														for digit in range(10					):
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      digit
																					result += reversible_numbers(
																					    0       ,		(remainder + 2 * digit) // 10       ,		A       ,		A					)
														return result

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							for digita in range(10					):
														SCREAMING_SNAKE_CASE       :   int         =      digita

														if (remainder + digita) % 2 == 0:
																					SCREAMING_SNAKE_CASE       :   str         =      ODD_DIGITS
														else:
																					SCREAMING_SNAKE_CASE       :   List[str]         =      EVEN_DIGITS

														for digita in other_parity_digits:
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      digita
																					result += reversible_numbers(
																					    remaining_length - 2       ,		(remainder + digita + digita) // 10       ,		A       ,		A       ,		)
							return result
def        UpperCAmelCase (    A	:					int = 9					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
							for length in range(1       ,		max_power + 1					):
														result += reversible_numbers(A       ,		0       ,		[0] * length       ,		A					)
							return result


if __name__ == "__main__":
							print(f'{solution() = }')




